Predictive modeling the free hydraulic jumps pressure through advanced statistical methods
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/217152 |
Resumo: | Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (_*X), pressures with different non-exceedance probabilities (P*k%), and the statistical coefficient of the probability distribution (Nk%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for _*X, and Nk%. The values of the Nk% coefficient indicated a single mean value for each probability. |
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Mousavi, Seyed NasrollahTeixeira, Eder DanielSteinke Júnior, RenatoBocchiola, DanieleNabipour, NarjesMosavi, AmirShamshirband, Shahabodin2021-01-08T04:06:48Z20202227-7390http://hdl.handle.net/10183/217152001115119Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (_*X), pressures with different non-exceedance probabilities (P*k%), and the statistical coefficient of the probability distribution (Nk%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for _*X, and Nk%. The values of the Nk% coefficient indicated a single mean value for each probability.application/pdfengMathematics. Basel: MDPI. Vol. 8, n. 3 (mar. 2020), 323, 16 p.Ressalto hidraulicoModelagem matemáticaFlutuações de pressãoModelos físicosVertedouroDistribuicao de probabilidadesMathematical modelingExtreme pressureHydraulic jumpStilling basinDeviation of pressure fluctuationsStatistical coefficient of the probability distributionPredictive modeling the free hydraulic jumps pressure through advanced statistical methodsEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001115119.pdf.txt001115119.pdf.txtExtracted Texttext/plain56903http://www.lume.ufrgs.br/bitstream/10183/217152/2/001115119.pdf.txt4160434118165c7ca08bf135c5f4f338MD52ORIGINAL001115119.pdfTexto completo (inglês)application/pdf4519322http://www.lume.ufrgs.br/bitstream/10183/217152/1/001115119.pdfad430b516f15f2922c537ba774092b1fMD5110183/2171522023-12-29 04:22:46.563757oai:www.lume.ufrgs.br:10183/217152Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-12-29T06:22:46Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Predictive modeling the free hydraulic jumps pressure through advanced statistical methods |
title |
Predictive modeling the free hydraulic jumps pressure through advanced statistical methods |
spellingShingle |
Predictive modeling the free hydraulic jumps pressure through advanced statistical methods Mousavi, Seyed Nasrollah Ressalto hidraulico Modelagem matemática Flutuações de pressão Modelos físicos Vertedouro Distribuicao de probabilidades Mathematical modeling Extreme pressure Hydraulic jump Stilling basin Deviation of pressure fluctuations Statistical coefficient of the probability distribution |
title_short |
Predictive modeling the free hydraulic jumps pressure through advanced statistical methods |
title_full |
Predictive modeling the free hydraulic jumps pressure through advanced statistical methods |
title_fullStr |
Predictive modeling the free hydraulic jumps pressure through advanced statistical methods |
title_full_unstemmed |
Predictive modeling the free hydraulic jumps pressure through advanced statistical methods |
title_sort |
Predictive modeling the free hydraulic jumps pressure through advanced statistical methods |
author |
Mousavi, Seyed Nasrollah |
author_facet |
Mousavi, Seyed Nasrollah Teixeira, Eder Daniel Steinke Júnior, Renato Bocchiola, Daniele Nabipour, Narjes Mosavi, Amir Shamshirband, Shahabodin |
author_role |
author |
author2 |
Teixeira, Eder Daniel Steinke Júnior, Renato Bocchiola, Daniele Nabipour, Narjes Mosavi, Amir Shamshirband, Shahabodin |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Mousavi, Seyed Nasrollah Teixeira, Eder Daniel Steinke Júnior, Renato Bocchiola, Daniele Nabipour, Narjes Mosavi, Amir Shamshirband, Shahabodin |
dc.subject.por.fl_str_mv |
Ressalto hidraulico Modelagem matemática Flutuações de pressão Modelos físicos Vertedouro Distribuicao de probabilidades |
topic |
Ressalto hidraulico Modelagem matemática Flutuações de pressão Modelos físicos Vertedouro Distribuicao de probabilidades Mathematical modeling Extreme pressure Hydraulic jump Stilling basin Deviation of pressure fluctuations Statistical coefficient of the probability distribution |
dc.subject.eng.fl_str_mv |
Mathematical modeling Extreme pressure Hydraulic jump Stilling basin Deviation of pressure fluctuations Statistical coefficient of the probability distribution |
description |
Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (_*X), pressures with different non-exceedance probabilities (P*k%), and the statistical coefficient of the probability distribution (Nk%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for _*X, and Nk%. The values of the Nk% coefficient indicated a single mean value for each probability. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020 |
dc.date.accessioned.fl_str_mv |
2021-01-08T04:06:48Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/217152 |
dc.identifier.issn.pt_BR.fl_str_mv |
2227-7390 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001115119 |
identifier_str_mv |
2227-7390 001115119 |
url |
http://hdl.handle.net/10183/217152 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Mathematics. Basel: MDPI. Vol. 8, n. 3 (mar. 2020), 323, 16 p. |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
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UFRGS |
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Repositório Institucional da UFRGS |
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Repositório Institucional da UFRGS |
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http://www.lume.ufrgs.br/bitstream/10183/217152/2/001115119.pdf.txt http://www.lume.ufrgs.br/bitstream/10183/217152/1/001115119.pdf |
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